AMI-FML: A Privacy-Preserving Federated Machine Learning Framework for AMI

TL;DR

This paper introduces AMI-FML, a federated learning framework that enhances energy demand forecasting accuracy in smart grids while preserving user privacy by exchanging model weights instead of raw data.

Contribution

It proposes a novel privacy-preserving federated learning architecture for AMI that improves load forecasting accuracy using LSTM models and addresses communication efficiency.

Findings

Federated learning improves short-term load forecasting accuracy.

Model weight exchange preserves user privacy effectively.

Framework is suitable for slow network connections in AMI.

Abstract

Machine learning (ML) based smart meter data analytics is very promising for energy management and demand-response applications in the advanced metering infrastructure(AMI). A key challenge in developing distributed ML applications for AMI is to preserve user privacy while allowing active end-users participation. This paper addresses this challenge and proposes a privacy-preserving federated learning framework for ML applications in the AMI. We consider each smart meter as a federated edge device hosting an ML application that exchanges information with a central aggregator or a data concentrator, periodically. Instead of transferring the raw data sensed by the smart meters, the ML model weights are transferred to the aggregator to preserve privacy. The aggregator processes these parameters to devise a robust ML model that can be substituted at each edge device. We also discuss strategies to enhance privacy and improve communication efficiency while sharing the ML model parameters, suited for relatively slow network connections in the AMI. We demonstrate the proposed framework on a use case federated ML (FML) application that improves short-term load forecasting (STLF). We use a long short-term memory(LSTM) recurrent neural network (RNN) model for STLF. In our architecture, we assume that there is an aggregator connected to a group of smart meters. The aggregator uses the learned model gradients received from the federated smart meters to generate an aggregate, robust RNN model which improves the forecasting accuracy for individual and aggregated STLF. Our results indicate that with FML, forecasting accuracy is increased while preserving the data privacy of the end-users.